{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example Q5: Experiment Sweeps\n",
    "This example notebook shows how to add sweeps to Auspex qubit experiments\n",
    "\n",
    "© Raytheon BBN Technologies 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import auspex.config as config\n",
    "config.auspex_dummy_mode = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AWG_DIR environment variable not defined. Unless otherwise specified, using temporary directory for AWG sequence file outputs.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auspex-WARNING: 2019-04-02 14:33:37,114 ----> Could not load channelizer library; falling back to python methods.\n"
     ]
    }
   ],
   "source": [
    "from QGL import *\n",
    "from auspex.qubit import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Channel library setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auspex-INFO: 2019-04-02 14:33:37,563 ----> Could not find an existing pipeline. Please create one.\n"
     ]
    }
   ],
   "source": [
    "cl = ChannelLibrary(db_resource_name=\":memory:\")\n",
    "pl = PipelineManager()\n",
    "\n",
    "q1 = cl.new_qubit(\"q1\")\n",
    "aps2_1 = cl.new_APS2(\"BBNAPSa\", address=\"192.168.2.4\", trigger_interval=200e-6) \n",
    "aps2_2 = cl.new_APS2(\"BBNAPSb\", address=\"192.168.2.2\")\n",
    "dig_1  = cl.new_Alazar(\"Alazar_1\", address=\"1\", sampling_rate=500e6, record_length=1024)\n",
    "h1 = cl.new_source(\"Holz_1\", \"HolzworthHS9000\", \"HS9004A-009-1\", reference='10MHz', power=-30)\n",
    "h2 = cl.new_source(\"Holz_2\", \"HolzworthHS9000\", \"HS9004A-009-2\", reference='10MHz', power=-30)\n",
    "\n",
    "cl.set_measure(q1, aps2_1, dig_1.ch(\"1\"), trig_channel=aps2_1.ch(\"m2\"), gate=False, generator=h1)\n",
    "cl.set_control(q1, aps2_2, generator=h2)\n",
    "cl.set_master(aps2_1, aps2_1.ch(\"m1\"))\n",
    "cl[\"q1\"].measure_chan.frequency = 0e6\n",
    "cl[\"q1\"].measure_chan.autodyne_freq = 10e6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pipeline setup: **Take Note:** we use the `buffers` keyword argument to automatically generate buffers instead of writers. This is sometimes convenient if you don't require data to be written to file. It becomes immediately available in the notebook after running!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pl.create_default_pipeline(buffers=True)\n",
    "pl[\"q1\"].add(Display(label=\"blub\"))\n",
    "pl[\"q1\"][\"Demodulate\"][\"Integrate\"].add(Display(label=\"int\", plot_dims=1))\n",
    "pl.show_pipeline()"
   ]
  },
  {
   "attachments": {
    "Screen%20Shot%202019-04-05%20at%204.49.06%20PM.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screen%20Shot%202019-04-05%20at%204.49.06%20PM.png](attachment:Screen%20Shot%202019-04-05%20at%204.49.06%20PM.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize software demodulation parameters. If these are not properly configured than the `Channelizer` filter will report 'insufficient decimation' or other errors. The integration boxcar parameters are then defined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "demod = pl[\"q1\"][\"Demodulate\"]\n",
    "demod.frequency = cl[\"q1\"].measure_chan.frequency\n",
    "demod.decimation_factor = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "integ = pl[\"q1\"][\"Demodulate\"][\"Integrate\"]\n",
    "integ.box_car_start = 0.2e-6\n",
    "integ.box_car_stop= 1.9e-6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding experiment sweeps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once a `QubitExperiment` has been created, we can programmatically add sweeps as shown here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "lengths = np.linspace(20e-9, 2020e-9, 31)\n",
    "exp = QubitExperiment(RabiWidth(q1,lengths),averages=50)\n",
    "exp.set_fake_data(dig_1, np.exp(-lengths/1e-6)*np.cos(1e7*lengths))\n",
    "# exp.add_qubit_sweep(q1,\"measure\", \"frequency\", np.linspace(6.512e9, 6.522e9, 11))\n",
    "exp.run_sweeps()"
   ]
  },
  {
   "attachments": {
    "Screen%20Shot%202019-03-13%20at%2012.37.11%20PM.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screen%20Shot%202019-03-13%20at%2012.37.11%20PM.png](attachment:Screen%20Shot%202019-03-13%20at%2012.37.11%20PM.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fetch the data and data descriptor directly from the buffer. The data is automatically reshaped to match the experiment axes, and the descriptor enumerates all of the values of these axes for convenience plotting, etc.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data, descriptor = exp.outputs_by_qubit[\"q1\"][0].get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<SweepAxis(name=q1 measure frequency,length=11,unit=None,value=6522000000.0,unstructured=False>,\n",
       " <DataAxis(name=delay, start=0.02, stop=2.02, num=21, unit=us)>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptor.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 21)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We even include a convenience `extent` function conforming to the infinitely forgettable matplotlib format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.imshow(np.real(data), aspect='auto', extent=descriptor.extent())\n",
    "plt.xlabel(\"Delay (µs)\")\n",
    "plt.ylabel(\"Frequency (GHz)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Multiple Sweeps\n",
    "\n",
    "An arbitrary number of sweeps can be added. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiled 21 sequences.\n"
     ]
    }
   ],
   "source": [
    "exp = QubitExperiment(RabiWidth(q1,lengths),averages=50)\n",
    "exp.add_qubit_sweep(q1,\"measure\", \"frequency\", np.linspace(6.512e9, 6.522e9, 5))\n",
    "exp.add_qubit_sweep(q1,\"measure\", \"amplitude\", np.linspace(0.0, 1.0, 21))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we inspect the internal representation of the \"output connector\" into which the instrument driver will dump its data, we can see all of the axes it contains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<SweepAxis(name=q1 measure amplitude,length=21,unit=None,value=0.0,unstructured=False>,\n",
       " <SweepAxis(name=q1 measure frequency,length=5,unit=None,value=6512000000.0,unstructured=False>,\n",
       " <DataAxis(name=averages, start=0, stop=49, num=50, unit=None)>,\n",
       " <DataAxis(name=delay, start=0.02, stop=2.02, num=21, unit=us)>,\n",
       " <DataAxis(name=time, start=0.0, stop=2.046e-06, num=1024, unit=None)>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.output_connectors[\"q1\"].descriptor.axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `DataAxis` entries are \"baked in\" using hardware looping, while the `SweepAxis` entries are external software loops facilitated by Auspex."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
